Volumetric assessment of lymph node metastases in patients with non-seminomatous germ cell tumors treated with chemotherapy
Bibliographic record
Abstract
INTRODUCTION: We evaluate volumetry and RECIST (Response Evaluation Criteria In Solid Tumors) as methodologies for response after chemotherapy for non-seminomatous germ cell tumour with retroperitoneal lymph node metastases. METHODS: We performed a retrospective analysis of non-seminomatous testicular tumours and concurrent retroperitoneal lymph node metastases, which received chemotherapy and had computed tomography scans before and after treatment. Volumetric analysis and RECIST criteria were used to calculate response rates. We included a new category (favourable response) for patients with response rates between <100% and >70%. We calculated the correlation between volumetric and RECIST criteria with histological and clinical variables. RESULTS: In total, 18 patients met the inclusion criteria. Histopathologic analysis of orchiectomy showed teratoma in 55.5% of patients, and those without teratoma had predominantly embryonal carcinoma. The mean baseline volume of retroperitoneal metastases was 447 cc, the mean post-chemotherapy volume was 33.6 cc, and the response rate was 62.6%. According to RECIST criteria, the mean baseline diameter was 4.93 cm, the mean post-chemotherapy diameter was 2.39 cm, and the response rate was 42.4%. Large post-chemotherapy residual masses correlated in both classifications with teratoma. The response rate was associated with the need for surgical treatment and the volumetric classification correlated with the need for lymphadenectomy. CONCLUSIONS: This study evaluated volumetry as a way to measure clinical response in lymph node metastases of non-seminomatous germ cell tumours. Volumetric analysis is the next step in the evaluation of response rate; its accuracy remains to be determined. Teratoma had greater residual masses and our classification correlated with the need for lymphadenectomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".